考虑松动圈的地下洞室群施工期岩体参数反演

结合大岗山水电站地下厂房洞室群施工期分层开挖特点,提出考虑松动圈参数弱化效应的反演基本模型。引入GA-BP算法,基于正交试验设计与FLAC^3D差分程序获得神经网络学习样本及测试样本,建立起围岩力学参数与位移之间的高度非线性映射关系。根据现场实测位移和网络映射位移,建立目标函数,采用遗传算法在岩体参数经验取值范围内,搜索使目标函数取得最优解的参数组合,以获取反演的最优岩体参数。最后利用获取的岩体参数进行正演分析并进行位移的后验差法检验,结论表明了该方法的适用性,其可用于反馈并指导地下工程设计与施工。

Intelligent Back Analysis of Rockmass Parameters of Underground Caverns in Consideration of Loose Zone During Construction

A basic back analysis model is proposed in consideration of the weakening effect of loose zone parameters for Dagangshan underground powerhouse caverns excavated in layers. The learning and testing samples of neural network were acquired by introducing GA BP algorithm based on orthogonal experimental design and FLAC 3D differential procedures. Hence a highly nonlinear mapping relation between the rock’s mechanical parameters and the displacements was established. According to field measured displacement and network mapping displacement, the objective function was obtained. By using genetic algorithm, the optimum rockmass parameters were obtained by searching parameter combinations which can make the network mapping displacement and the field measured displacement satisfy the optimal solution of the objective function within the range of empirical values. Finally, forward analysis on the obtained rockmass parameters and backward error detection on the displacements were carried out. Results show that the method is suitable for the design and construction of underground works.